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Adipose tissue composition determines its computed tomography radiodensity

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Adipose tissue radiodensity in computed tomography (CT) performed before surgeries can predict surgical difficulty. Despite its clinical importance, little is known about what influences radiodensity. This study combines desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and electrospray ionization (ESI) with machine learning to unveil how chemical composition of adipose tissue determines its radiodensity.

Methods

Patients in the study underwent abdominal surgeries. Before surgery, CT radiodensity of fat near operated sites was measured. Fifty-three fat samples were collected and analyzed by DESI-MSI, ESI, and histology, and then sorted by radiodensity, demographic parameters, and adipocyte size. A non-negative matrix factorization (NMF) algorithm was developed to differentiate between high and low radiodensities.

Results

No associations between radiodensity and patient age, gender, weight, height, or fat origin were found. Body mass index showed negative correlation with radiodensity. A substantial difference in chemical composition between adipose tissues of high and low radiodensities was observed. More radiodense tissues exhibited greater abundance of high molecular weight species, such as phospholipids of various types, ceramides, cholesterol esters and diglycerides, and about 70% smaller adipocyte size. Less radiodense tissue showed high abundance of short acyl-tail fatty acids.

Conclusions

This study unveils the connection between abdominal adipose tissue radiodensity and its chemical composition. Because the radiodensity of the fat around the surgical site is associated with surgical difficulty, it is important to understand how adipose tissue composition affects this parameter. We conclude that fat tissue with a higher content of various phospholipids and waxy lipids is more CT radiodense.

Clinical relevance statement

This study establishes the connection between the CT radiodensity of adipose tissue and its chemical composition. Clinicians may use this information for preoperative planning of surgical procedures, potentially modifying their surgical approach (for example, performing partial nephrectomy openly rather than laparoscopically).

Key Points

• Adipose tissue radiodensity values in computed tomography images taken prior to the surgery can potentially predict surgery difficulty.

• Fifty-three human specimens were analyzed by advanced mass spectrometry, molecular imaging, and machine learning to establish the key features that determine Hounsfield units’ values of adipose tissue.

• The findings of this research will enable clinicians to better prepare for surgical procedures and select operative strategies.

Graphical abstract

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Abbreviations

BMI:

Body mass index

CNN:

Convolutional neural networks

CT:

Computed tomography

DESI-MSI:

Desorption electrospray ionization mass spectrometry imaging

ESI:

Electrospray ionization

FWHM:

Full width at half maximum

H&E:

Hematoxylin and eosin

HCD:

Higher energy collisional dissociation

HU:

Hounsfield units

LC-MS:

Liquid chromatography–mass spectrometry

LR:

Linear regression

m/z :

Mass to charge ratio

ML:

Machine learning

MS:

Mass spectrometry

MSE:

Mean square error

NMF:

Non-negative matrix factorization algorithm

Q-TOF:

Quadrupole time-of-flight

SD:

Standard deviation

SIM:

Selected ion monitoring

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Acknowledgements

This research was conducted using the Spatial Metabolomics Unit founded with the help of the Wolfson Foundation and the Wolfson Foundation Charity Trust (award reference PR/oys/jw/md/eh/22747/22641) and the Metabolomics Core Unit, Shared Research Facility, Faculty of Medicine-Hebrew University. This research was supported by Israel Science Foundation (grant number 1840/20), United States–Israel Binational Science Foundation (grant number 2019237), Israel Cancer Research Fund (grant number 20-204-RCDA), Council for Higher Education, Israel (Alon Fellowship), David R. Bloom Center for Pharmaceutical Sciences and The Alex Grass Center for Drug Design and Synthesis.

Funding

The authors state that this work has not received any funding in addition to the grants listed in the Acknowledgements.

Author information

Author notes

  1. Amani Zoabi and Einav Bentov-Arava contributed equally.

Authors and Affiliations

  1. The Institute for Drug Research, the School of Pharmacy, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel

    Amani Zoabi, Einav Bentov-Arava, Adan Sultan & Katherine Margulis

  2. Department of Pathology, Hadassah Medical Center, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel

    Anna Elia

  3. Metabolomics Center, Core Research Facility, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel

    Ori Shalev

  4. Nuclear Medicine, Hadassah-Hebrew University Medical Center, Jerusalem, Israel

    Marina Orevi

  5. Department of Urology, Hadassah Medical Center the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel

    Ofer N. Gofrit

Authors
  1. Amani Zoabi
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  3. Adan Sultan
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  4. Anna Elia
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  8. Katherine Margulis
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Corresponding author

Correspondence to Katherine Margulis.

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Guarantor

The scientific guarantor of this publication is O. Gofrit.

Conflict of Interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and Biometry

One of the authors (E. Bentov-Arava) has significant statistical expertise.

No complex statistical methods were necessary for this paper.

Informed Consent

Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

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Methodology

• Prospective

• Observational

• One Institution study

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Zoabi, A., Bentov-Arava, E., Sultan, A. et al. Adipose tissue composition determines its computed tomography radiodensity. Eur Radiol 34, 1635–1644 (2024). https://doi.org/10.1007/s00330-023-09911-7

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